Yassine Zaoui Seghroucheni

Exploitation of the recommendation systems in the calculation of the learning path

In this paper we present a model of an adapting learning system based on a recommendation system, operating after assessment to correct learning path of the learners who have experienced difficulties in the assessment. Our approach aims to correct the learning path of learners who have not passed the assessment successfully, by calculating the similarities in behaviors between them and those who did.

A Recommendation System Operating after Assessment to Correct Learning Paths in a Content Adapting System

In this paper we present a model of an adapting learning system based on a recommendation system, operating after assessment to correct learning path of the learners who have experienced difficulties in the assessment. Our approach aims to correct the learning path of the learners who have failed at the assessment, by calculating the similarities in behaviors between them and those who did, then recommend them the learning objects that can build the most relevant learning path .

ALS_CORR[LP]: An adaptive learning system based on the learning styles of Felder-Silverman and a Bayesian network

The aim of this paper is to present the adaptive learning system called ALS_CORR[LP]1. This system belongs to a very specific class of the e-learning systems, which is the adaptive learning ones. In fact they have the ability to adapt the learning process according to each learner needs, learning styles, objectives, etc. ALS_CORR[LP] is based on the learner prerequisites and the learning styles of Felder-Silverman, to design the learner model. As for the domain model, it is designed according to the recommendations of the differentiated pedagogy, which advocates creating multiple versions of the same learning object. Finally in order to ensure the adaptation inside the system, a Bayesian network, to match the designed learning object with the specifics of the learner profile was developed. It is also necessary to emphasize, that the major feature of the system is, its ability to correct the generated learning path in case of a failure in the evaluation phase. The learning path relevance is questioned, based on a recommendation system which enables updating the initial profile, or recommending the most relevant versions of the learning object, in case where the similarity calculation in behavior, reveals that the observed behavior in the system does not fit the initial profile description.

An adaptive learning system based on a Job model, the differentiated instruction and Felder and Silverman's learning styles model

In this paper we propose a model that uses Job needs and organizations requirement to define an e-learning path that help the learner to be able to satisfy the job targeted. We review existing literature on adaptive learning systems, thereby; we propose a model using the recommendation of differentiated pedagogy, also the learners profile and the Job characteristics. In this model, the system can offer the user a suggested learning path to meet appropriate learning of a job objective.